Parallel Hopfield networks.
We introduce a novel type of neural network, termed the parallel Hopfield network, that can simultaneously effect the dynamics of many different, independent Hopfield networks in parallel in the same piece of neural hardware. Numerically we find that under certain conditions, each Hopfield subnetwork has a finite memory capacity approaching that of the equivalent isolated attractor network, while a simple signal-to-noise analysis sheds qualitative, and some quantitative, insight into the workings (and failures) of the system.
Wilson
Robert C
RC
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19103, USA. rcwilson@seas.upenn.edu
D000465:Algorithms; D001245:Association Learning / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008568:Memory / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics